Transactions of Nonferrous Metals Society of China
JOURNAL OF RAILWAY SCIENCE AND ENGINEERING
|Vol. 17 No. 5 October 2007|
（School of Information Science and Engineering, Central South University, Changsha 410083, China ）
Abstract:Due to the importance of detecting the matte grade in the copper flash smelting process, the mechanism model was established according to the multi-phase and multi-component mathematic model. Meanwhile this procedure was a complicated production process with characteristics of large time delay, nonlinearity and so on. A fuzzy neural network model was set up through a great deal of production data. Besides a novel constrained gradient descent algorithm used to update the parameters was put forward to improve the parameters learning efficiency. Ultimately the self-adaptive combination technology was adopted to paralleled integrate two models in order to obtain the prediction model of the matte grade. Industrial data validation shows that the intelligently integrated model is more precise than a single model. It can not only predict the matte grade exactly but also provide optimal control of the copper flash smelting process with potent guidance.
Key words: copper flash smelting process; matte grade; multi-phase and multi-component model; fuzzy neural network; constrained gradient descent algorithm